Impact of Empowering Leadership on Organizational Performance

Empowering Leadership nowadays is considered one of the most important features of the human resources in an organization in order to boost the productivity of the employees and maximize the performance of the organizations. However, this research will study the importance of empowering leadership and their effect on the performance of the organization as a whole. The research will use quantitative method for collecting data, and then analyzing them using regression analysis and Z-Test in the SPSS statistical tool in order to maintain the results and validate the hypothesis of the research. The findings of the study stated that motivation, job satisfaction, compensation benefits and performance appraisals have a direct effect on leadership in the workplace. The higher the leadership is in the workplace, the higher the performance of the employees will be

Topp-Leone Weibull-Lomax distribution: Properties, Regression Model and Applications

In this paper, a new four parameter lifetime distribution called the Topp Leone Weibull-Lomax distribution is introduced. Some mathematical properties of the new distribution are studied including the quantile function, ordinary and incomplete moments, probability weighted moment, conditional moments, order statistics, stocastic ordering and stress strength reliability parameter. The regression model and the residual analysis for the new model are also investigated. The model parameters are estimated by using the maximum likelihood criterion and the behavior of these estimates are examined by conducting a simulation study. We prove empirically the importance and flexibility of the new distribution in modeling four data sets

Anomaly Detection in Time Series: Current Focus and Future Challenges

Anomaly detection in time series has become an increasingly vital task, with applications such as fraud detection and intrusion monitoring. Tackling this problem requires an array of approaches, including statistical analysis, machine learning, and deep learning. Various techniques have been proposed to cater to the complexity of this problem. However, there are still numerous challenges in the field concerning how best to process high-dimensional and complex data streams in real time. This chapter offers insight into the cutting-edge models for anomaly detection in time series. Several of the models are discussed and their advantages and disadvantages are explored. We also look at new areas of research that are being explored by researchers today as their current focuses and how those new models or techniques are being implemented in them as they try to solve unique problems posed by complex data, high-volume data streams, and a need for real-time processing. These research areas will provide concrete examples of the applications of discussed models. Lastly, we identify some of the current issues and suggest future directions for research concerning anomaly detection systems. We aim to provide readers with a comprehensive picture of what is already out there so they can better understand the space – preparing them for further development within this growing field

Proceedings of the Graduate Student Symposium of the 7th International Conference on the Theory and Application of Diagrams, July 5 2012

Proceedings of the Graduate Student Symposium held at the 7th International Conference on the Theory and Application of Diagrams, ( Diagrams 2012 ), held at the University of Kent on July 5, 2012. Dr. Nathaniel Miller, professor of in the School of Mathematical Sciences at UNC, served on the symposium organizing committee

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

AN ECO-LINGUISTIC ANALYSIS OF PAKISTANI ADVERTISEMENTS: A GENDER BASED STUDY

The study of Eco-Friendly Messages in Pakistani Ads delves into the influence of eco-friendly messaging on consumer behavior, specifically focusing on gender differences in the Pakistani context. Against the backdrop of a rising global environmental consciousness, Pakistani advertisers have incorporated eco-friendly messaging to resonate with an increasingly environmentally aware audience. This research explores the effectiveness of such advertisements compared to traditional approaches, aiming to uncover the motivations behind consumer choices and communication strategies within these ads. Through a careful analysis of ad content and by posing questions, the study examines how messages about environmental consciousness were encoded and decoded, emphasizing the strategic promotion of eco-friendly products in alignment with gender-specific preferences. Key findings indicate nuanced differences, with a slightly higher percentage of men expressing a liking for eco-friendly ads, yet women showing a higher inclination towards regular products in their purchasing decisions. These insights highlight the significance of tailoring advertising strategies to specific audiences, offering valuable guidance for advertisers seeking to navigate gender dynamics and promote sustainability in a diverse and dynamic market like Pakistan. Ultimately, the study contributes to a deeper understanding of the evolving landscape of advertising in Pakistan and provides practical insights for crafting impactful campaigns that align with consumer preferences and environmental sustainability